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Derivation and Application of a Cost Model for Systems Engineering Cost Estimation(Derivación y Aplicación de un Modelo de Estimación de Costos para la Ingeniería de Sistemas)
Prof. Ricardo ValerdiSystems & Industrial EngineeringUniversity of Arizona 9 Enero 2017
Academia de IngenieríaPalacio de Minería
México DF
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Principle #1
A solution that is too expensive, is not a solution.
Principle #2
Cost of products are a function of complexity and size.
Project Management Triangle
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Performance
CostSchedule
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5Nota: Producto Bruto Interno en Mexico para proyectos militares = 0.7%http://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS
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How is Systems Engineering Defined?
• Acquisition and Supply – Supply Process– Acquisition Process
• Technical Management– Planning Process– Assessment Process– Control Process
• System Design– Requirements Definition Process– Solution Definition Process
• Product Realization– Implementation Process– Transition to Use Process
• Technical Evaluation– Systems Analysis Process
– Requirements Validation Process– System Verification Process– End Products Validation Process
EIA/ANSI 632, Processes for Engineering a System, 1999.
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COSYSMO Data Sources (2002-present)Boeing Integrated Defense Systems (Seal Beach, CA)
Raytheon Intelligence & Information Systems (Garland, TX)Missile Systems (Tucson, AZ)
Northrop Grumman Mission Systems (Redondo Beach, CA)
Lockheed Martin Transportation & Security Solutions (Rockville, MD)Integrated Systems & Solutions (Valley Forge, PA)Systems Integration (Owego, NY)Aeronautics (Marietta, GA)Maritime Systems & Sensors (Manassas, VA; Baltimore, MD; Syracuse, NY)
General Dynamics Maritime Digital Systems/AIS (Pittsfield, MA)Surveillance & Reconnaissance Systems/AIS (Bloomington, MN)
BAE Systems National Security Solutions/ISS (San Diego, CA)
Information & Electronic Warfare Systems (Nashua, NH)
SAIC Army Transformation (Orlando, FL)
Integrated Data Solutions & Analysis (McLean, VA)
L-3 Communications Greenville, TX
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COSYSMO Scope• Addresses first four phases of the
system engineering lifecycle (per ISO/IEC 15288)
• Considers standard Systems Engineering Work Breakdown Structure tasks (per EIA/ANSI 632)
Conceptualize DevelopOper Test & Eval
Transition to Operation
Operate, Maintain, or Enhance
Replace orDismantle
EIA/ANSI 632, Processes for Engineering a System, 1999.
ISO/IEC 15288, System Life Cycle Processes, 2008
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COSYSMO
SizeDrivers
EffortMultipliers
Effort
Calibration
# Requirements# Interfaces# Scenarios# Algorithms
+3 Adj. Factors
- Application factors-8 factors
- Team factors-6 factors
COSYSMO Operational Concept
Valerdi (2005)
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Software Cost Estimating Relationship
cSaMM e ⋅⋅=
cKDSIMM ⋅⋅= 05.1)(4.2
Boehm, B. W., Software Engineering Economics, Prentice Hall, 1981.
MM = Man monthsa = calibration constantS = size driverE = scale factorc = cost driver(s)KDSI = thousands of delivered source instructions
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COSYSMO Model Form
∏∑=
⋅
Φ+Φ+Φ⋅=
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1,,,,,, )(
jj
E
kkdkdknknkekeNS EMwwwAPM
Where: PMNS = effort in Person Months (Nominal Schedule)
A = calibration constant derived from historical project data k = {REQ, IF, ALG, SCN}wx = weight for “easy”, “nominal”, or “difficult” size driver
= quantity of “k” size driverE = represents diseconomies of scaleEM = effort multiplier for the jth cost driver. The geometric product results in an
overall effort adjustment factor to the nominal effort.
xΦ
Valerdi (2005, 2008)
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UNDERSTANDING FACTORS– Requirements understanding – Architecture understanding– Stakeholder team cohesion – Personnel experience/continuity
COMPLEXITY FACTORS– Level of service requirements– Technology Risk– # of Recursive Levels in the Design– Documentation Match to Life Cycle Needs
OPERATIONS FACTORS– # and Diversity of Installations/Platforms– Migration complexity
PEOPLE FACTORS– Personnel/team capability – Process capability
ENVIRONMENT FACTORS– Multisite coordination – Tool support
Cost Driver Clusters
Valerdi (2005)
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Stakeholder team cohesion Represents a multi-attribute parameter which includes leadership, shared vision, diversity of stakeholders, approval cycles, group dynamics, IPT framework, team dynamics, trust, and amount of change in responsibilities. It further represents the heterogeneity in stakeholder community of the end users, customers, implementers, and development team.
1.5 1.22 1.00 0.81 0.65
Viewpoint Very Low Low Nominal High Very High
Culture Stakeholders with diverse expertise, task nature, language, culture, infrastructure Highly heterogeneous stakeholder communities
Heterogeneous stakeholder communitySome similarities in language and culture
Shared project culture
Strong team cohesion and project cultureMultiple similarities in language and expertise
Virtually homogeneous stakeholder communitiesInstitutionalized project culture
Compatibility Highly conflicting organizational objectives
Converging organizational objectives
Compatible organizational objectives
Clear roles & responsibilities
Strong mutual advantage to collaboration
Familiarity and trust
Lack of trust Willing to collaborate, little experience
Some familiarity and trust
Extensive successful collaboration
Very high level of familiarity and trust
Valerdi (2005)
Technology RiskThe maturity, readiness, and obsolescence of the technology being implemented. Immature or obsolescent technology will require more Systems Engineering effort.
Viewpoint Very Low Low Nominal High Very High
Lack of Maturity
Technology proven and widely used throughout industry
Proven through actual use and ready for widespread adoption
Proven on pilot projects and ready to roll-out for production jobs
Ready for pilot use Still in the laboratory
Lack of Readiness
Mission proven (TRL 9)
Concept qualified (TRL 8)
Concept has been demonstrated (TRL 7)
Proof of concept validated (TRL 5 & 6)
Concept defined (TRL 3 & 4)
Obsolescence
- Technology is the state-of-the-practice- Emerging technology could compete in future
- Technology is stale- New and better technology is on the horizon in the near-term
- Technology is outdated and use should be avoided in new systems- Spare parts supply is scarce
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Valerdi (2005)
Migration complexity This cost driver rates the extent to which the legacy system affects the migration complexity, if any. Legacy system components, databases, workflows, environments, etc., may affect the new system implementation due to new technology introductions, planned upgrades, increased performance, business process reengineering, etc.
Viewpoint Nominal High Very High Extra High
Legacy contractor
Self; legacy system is well documented. Original team largely available
Self; original development team not available; most documentation available
Different contractor; limited documentation
Original contractor out of business; no documentation available
Effect of legacy system on new system
Everything is new; legacy system is completely replaced or non-existent
Migration is restricted to integration only
Migration is related to integration and development
Migration is related to integration, development, architecture and design
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Valerdi (2005)
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Cost Drivers Ordered by Effort Multiplier Ratio (EMR)
Valerdi (2005)
Benefits of Local Calibration
Before local calibration
After local calibration
Systems Engineering Effort (SE Hours)
System Size (eReq)
Systems Engineering Effort (SE Hours)
System Size (eReq)
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Wang, G., Valerdi, R. and Fortune, J., “Reuse in Systems Engineering,” IEEE Systems Journal, 4(3), 376-384, 2010.
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Prediction AccuracyPRED(30)
PRED(25)
PRED(20)
PRED(30) = 100% PRED(25) = 57%
Valerdi (2005)
Academic prototype
Commercial Implementations
Proprietary Implementations
COSYSMO-R
SECOST
SEEMaP
ImpactAcademic Curricula
Intelligence CommunitySheppard Mullin, LLC
Policy & Contracts
Model
∏∑=
⋅
Φ+Φ+Φ⋅=
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1,,,,,, )(
jj
E
kkdkdknknkekeNS EMwwwAPM
COSYSMO
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20June 2004
Example:Company “Lockheed Grumman” is building a system that has:
COSYSMO
SizeDrivers
EffortMultipliers
36 Person Months of systemsengineeringeffort
Calibration
100 easy, 50 nominal, 75 difficult requirements2 easy, 3 difficult interfaces4 easy algorithms5 nominal operational scenarios
High requirements undHigh tech riskHigh process capability
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